Interesting WWW resources

If you find some nice blogs or links. let me know to add it here.

AI competitions

  1. Aliyun AI competitions
  2. ML in Medical analysis (https://grand-challenge.org/challenges/)
  3. ML in materiasl (https://matdat18.wordpress.ncsu.edu/projects/)
  4. ML in Kaggle (https://www.kaggle.com/competitions)
  5. Ai challenges (https://challenger.ai/competitions)
  6. Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

References

  1. best 50 free datasets for ML
  2. Pytorch tutorials collection
  3. Deep learning model zoo with code
  4. the-mostly-complete-chart-of-neural-networks-explained
  5. Neural network zoo
  6. Medium.com GAN tutorials applications
  7. cell types of ANN
  8. https://machinelearningmastery.com/category/python-machine-learning/
  9. http://www.fast.ai/ deep learning free courses
  10. Hongyi Lee's Deep learning tutorials and course materials.
  11. ML walk through with python After spending considerable time and money on courses, books, and videos, Iíve arrived at one conclusion: the most effective way to learn data science is by doing data science projects.
  12. Techgrabyte AI news
  13. Google deep learning collections
  14. 200 best ML tutorials
  15. https://machinelearningmastery.com
  16. Machine learning cheatsheet
  17. Machine learning news
  18. AAAI Machine learning news
  19. Carnegie Mellon Machine learning department
  20. ML in games
  21. ML blogs readings
  22. free video dataset earthcam.com
  23. Brief Summary of the Best Practices in Teaching
  24. https://grand-challenge.org Medical Image analysis challenges
  25. https://github.com/eriklindernoren/Keras-GAN
  26. http://blog.exbot.net/ ROS Robotics blogs
  27. https://towardsdatascience.com/machine-learning blog
  28. T-SNE visualization of high dimension data
  29. deep learning models with code
  30. 30 amazing ML projects
  31. deep reinforcement learning
  32. deep reinforcement learning and GAN
  33. Feature engineering/exploration tools
  34. Why You Need to Start Using Embedding Layers
  35. An overview of gradient descent optimization algorithms

Other ML course materials

  1. Uni.of Southern California ML 2009
  2. Wired AI news
  3. Techxplore news
  4. Techgrabyte AI news

Datasets

  1. Google dataset search

ML News

  1. phys.org CS news
  2. sciencedaily AI news
  3. Google ML/Deep learning community
  4. http://www.zhishifenzi.com/news/ai
  5. where is our curiosity?

Conferences

Top 3 Robotics conferences

  1. https://icra2018.org/
  2. https://www.iros2018.org/
  3. http://www.roboticsconference.org
  4. https://thegradient.pub/2018-rss-conference/
  5. https://ai.google/research/pubs/?area=Robotics
  6. https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow

Interesting applications

  1. Deep learning for music generation
  2. Neural style transfer resources
  3. https://blog.paperspace.com/art-with-neural-networks/
  4. Neural Style Transfer for Audio Spectograms
  5. Music Style Transfer: A Position Paper
  6. Conditional End-to-End Audio Transforms
  7. A Style-Aware Content Loss for Real-time HD Style Transfer
  8. ICLR2018 best 100 papers

Bioinformatics problems

[[PlayMolecule BindScope: large scale CNN-based virtual screening on the web |PlayMolecule BindScope: large scale CNN-based virtual screening on the web]]

K Deep: protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks.